Sub-Networks Design Our discriminator consists of 6 convolutional layers. The first 4 sequential layers extract the features from the input. Then, the feature are simultaneously fed into the last two convolutional layers for classification of real/fake and classification of tracer domain. On the other hand, our generator is a 3D Dual Squeeze-and-Excitation Network (DuSE-Net), and the architecture is based on a 3D U-Net [1]} with enhancements on 1.) spatial-wise and channel-wise feature re-calibration [2]}, [3]} and 2.) feature transformation in the latent space. Specifically, to better fuse the multi-channel input consisting of tracer domain volume, MR volume, and tracer volume as illustrated in Figure REF , we propose to use a 3D dual squeeze-and-excitation block at each 3D U-Net's outputs, such that channel-wise and spatial-wise features are iteratively re-calibrated and fused. In the latent space, we also utilize 3 sequential residual convolutional blocks to further transform the feature representation. Network architecture and implementation details are summarized in our supplementary.
